GAN training is notoriously unstable and prone to collapse; thus, the early NAS-GAN methods only search generator (G) with a fixed architecture of discriminator (D). Although there have been some studies on searching both G and D, there are still some unresolved problems. For example, AdversarialNAS [1]} searches G and D simultaneously in a differentiable way, but this mechanism results in highly coupled architectures of G and D and hence amplifies the instability of GAN training. The ablation study in [2]} has also demonstrated that simultaneously searching G and D would potentially increase the negative effect of inferior discriminators and hinder finding the optimal GANs. Liu et al. [3]} propose to progressively grow the architectures of G and D in an alternating fashion. However, their method suffers from huge computational costs (580 GPU days on the CIFAR-10 dataset). COEGAN [4]} is very relevant to our work, which also uses an evolutionary algorithm to search G and D in two separate groups of architectures (called populations). Since their two populations' architectures are coupled during search, to reduce the difficulty of searching, COEGAN only explores a very simple search space and experiments on a small dataset (MNIST). The final results show that COEGAN fails to outperform the previous human-designed GANs. Based on the above reviews, we believe that coupling G and D is not conducive to searching for the optimal GAN. Therefore, we propose a two-stage scheme to decouple the search of G and D. Fig. REF  presents the pipeline of our two-stage EAGAN, and Alg. REF  summarizes the detailed process.
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